What is Real-Time Analytics
Real-time analytics is the process which is used for analyzing the huge amount of data at the moment it is used or produced. In this, we extract valuable information for the organization as soon as it’s created or stored.
In other words, we can say that Real-time analytics is a process which mainly focuses on the data produced or consumed or stored within a live environment. Let’s take an example of analyzing the huge amount of data as it is produced within banks and branches, stock exchanges throughout the globe. The scope of analytics can be from multiple sources. We can import or fetch the data and store it within a system and can execute data analysis algorithms over it. And further, these analytics data is delivered to the users/administrator through an analytics dashboard. Real-time analytics can be used for below-listed purposes:-
- To report historical data and current data concurrently.
- For receiving alerts on the basis of certain and predefined parameters.
- To build operational decisions and apply them on business processes or on other production activities based on real-time and on an ongoing basis.
- To apply pre-existing prescriptive models or predictive models.
- For outlook of real-time displays or dashboards in real time on constantly changing datasets.
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Benefits of Real-Time Analytics
Data Visualization:- Set of historical datasets can be placed into a single screen in order to represent an overall point but on the other hand, streaming data can be visualized in such a way that it updates in real time in order to display what is occurring in each and every single second.
Business Insights:- When it’s about business, real-time analytics can be used for receiving alerts on the basis of certain and predefined parameters. For example, if any store there is a drop in sales, then an alert can be triggered to tell management about the serious problem.
Increase competitiveness:- Real-time analytics helps the companies to surpass competitors who are still based on batch processing analysis.
Security:- Take an example of fraud detection, fraud can be detected immediately whenever it happens and a proper safety precaution can be taken in order to limit the damage.
Limitations of Real-Time Analytics
Compatibility:- In the case of historical big data analytics, Hadoop is the most widely used tool but in case of streaming and real-time data it is not. The better options are the use of spark streaming, Apache Samza, Apache Flink, or Apache Storm.
System Failure:- In term of business, real-time analytics or handling a data at rapid rates is not an easy job. It could lead to faulty analysis or even sometimes system failure.
What is Real Time Streaming
Real-time streaming is defined as it is the process by which huge size/volumes of data are processed quickly such that a firm extracting the information from that particular data can react to changing conditions in real time.
In other words, we can say that real-time streaming is based on the queries that work on time and buffer windows. When we compare this real-time streaming process with traditional database model, then we found that there is a lot of differences between these two processes. Both processes are opposite to each other. Real-time streaming makes use of data while in motion through the server but on the other hand in traditional database model data was first stored and indexed and was then processed.
Real-time analytics are used in many applications. Below listed are one of the major applications in which real-time streaming are used:-
- Pricing and analytics
- Network Monitoring
- Risk Management
- Fraud Detection
Real-Time Streaming Data
These days, there is a large number of Data Processing platforms available in order to process data from ingestion platforms. Some of them support streaming of data and some of them support real streaming of data which is generally also known as Real-Time data.
Streaming is defined as that we can instantly process the data as it arrives and then do processing and analyzing it at ingestion time. But in case of streaming, there is always some amount of delay in streaming data from ingestion layer. But on the other hand, in case of Real-Time data, have tight deadlines regarding time. So, in other words, Real-Time data or real streaming can be defined as if our platform capture any event within one microsecond then it is considered real streaming.
Whenever we talked about detecting frauds, predicting errors, business decisions and analyzing real-time logs, all the are the scenarios/examples of streaming. As soon as data arrives, it will receive instant and this is what we termed as Real-Time data.
Why We Need Real-Time Streaming?
We need Real Time Streaming because we all already know that distributed file systems such as Hadoop, S3 and other distributed file systems are supporting data processing in large volumes and on the other hand we can also query them using their different frameworks like Hive which uses MapReduce as their execution engine.
A lot of organizations are trying to collect as much data as they can regarding their products, services or even their organizational activities like tracking employees activities through various methods used like log tracking, taking screenshots at regular intervals.
So with the help of this data, Data Engineering allows us to convert this data into some particular basic formats and further data analysts turn this data into some useful results which help the organization in several ways such helping in improving their customer experiences and also boost their employee’s productivity.
But on the other hand whenever we are talking about real-time analytics, fraud detection, or log analytics, then this is not the way we want our data to be processed. The actual value data is in processing or acting upon it at the instant it receives.
Let’s take an example that we have a warehouse like Hive which has petabytes of data in it. But it only allows us to just only analyze our historical data and predict the future.
Processing this type of huge volumes of data is not more than enough. We need to process this type of data in real-time also so that business decisions can be taken immediately whenever an important event occurs by the organization. This is required in fraud detection, intelligence and surveillance systems, etc.
Earlier these type of constant streams of data at high ingestion rate is managed by firstly storing this data into some warehouse and then further running analytics on it.
But on the other hand, organizations are looking for such type of platforms where they can look into business insights in real-time and act upon them in real-time. Alerting systems and platforms are also based on these real-time streams. But the Effectiveness of these platform based on the data that we are processing in real-time.
Real-Time Analytics Platform
Real-time analytics platform consists of the following steps:-
- Real-Time Stream Sources
- Real-Time Stream Ingestion
- Real-Time Stream Storage
- Real-Time Stream Processing
Real-Time Stream Sources
For real-time analytics, the first major need sources from where real-time data is obtained. There are many sources of streaming data:-
The sensor is the output of the device that measures a physical quantity and transforms it into a digital signal.
Social Media Stream
Social media streaming like a Twitter feed, Facebook, Instagram, Youtube, Pinterest, Tumblr.
The stream which contains the data about which pages the website visits and in what order.
Real-Time Stream Ingestion
Now there is a need to ingest the streams which are coming from the real-time stream sources. So there various open source tools in the market through which we can ingest the stream and some of them are listed below:-
In simple words, we can say that Apache NiFi is a data ingestion tool. It is an integrated data logistics platform for automating the movement of data between disparate systems. It provides real-time control that makes it easy to manage the movement of data between any source and any destination. Apache NiFi supports disparate and distributed sources of differing formats, schemas, protocols, speeds, and sizes such as machines, geolocation devices, click streams, files, social feeds, log files and videos and more. It is configurable plumbing for moving data around, similar to how FedEx, UPS or other courier delivery services move parcels around. Apache NiFi also allows us to trace our data in real time, just like we could trace a delivery.
StreamSets is also a data ingestion tool similar to NIFI. Basically, StreamSets is data operations platform where we can efficiently develop batch and streaming dataflows, and further operate them with full visibility and control and easily evolve our architecture over time.
Real-Time Stream Storage
Further, we need storage in which we can ingest stream. There are many open source stream storages which are available in the market. Some of them are listed below:-
Kafka is used for building real-time data pipelines and streaming apps. It is horizontally scalable, fault-tolerant, wicked fast, and runs in production in thousands of companies.
Apache Pulsar is an open-source distributed pub-sub messaging system originally created at Yahoo and now part of the Apache Software Foundation.
NATS Server is a simple, high-performance open source messaging system for cloud-native applications, IoT messaging, and microservices architectures.
Real-Time Stream Processing
There are some open source data streaming platforms which are available in the market which are used for processing the streaming data and some of them are listed below:-
Apache Spark is a unified analytics engine for large scale data processing. Basically, Apache Spark is a computing technology which is specially designed for faster computation. Spark is designed in order to cover batch applications, interactive queries, algorithms, and streaming. The main feature of spark is that it is in-memory cluster computing which means that this will increase the processing speed of an application.
Apache Apex is also a unified stream and batch processing engine. Apache Apex is designed to process data in motion, in distributed and in a tolerant way. Basically, Apache Apex is based on separate functional and operational specifications rather compounding them together.
Apache Flink is an open source stream processing framework for distributed, high performance and data accurate data streaming applications. Apache Flink also supports batch processing as a special case of stream processing.
Similar to above processing systems, Apache Storm is also free and open-source distributed real-time computation system. The storm is very simple and also can be used with any of programming languages. Storm is extremely fast, with the ability to process over a million records per second per node on a cluster of modest size. The main features of Storm are that it is fast, scalable, fault-tolerant, reliable and easy to operate.
Basically, Apache Bean is a unified programming model which is used for implementing batch and streaming data processing jobs that run on any execution engine. The main features of Apache Beam are that it is unified, portable and Extensible and it works with any processing engine like Apache Spark, Apache Flink, Apache Apex, Google Cloud Dataflow, and Apache Gear pump.
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